Key capabilities | MLOps
Deploy and manage AI apps at scale with MLOps
The true impact of AI insights happens when models are sent into production. Deploying insights to decision dashboards or enterprise systems is the aha moment when data turns into value. Robust MLOPs streamlines the process to increase the speed of model deployment and usability.
Synchronize the handoff from data science to machine learning application
A common MLOps environment encourages a collaborative environment for data scientists and machine learning engineers. Streamline and accelerate the integration and deployment of ML models with proper monitoring, validation, and governance.
Send models to deployment in a single click
Activate batch or real-time insights with the deployment manager that packages and sends everything needed for the AI model to transition from the design and development environment to the production environment.
Model monitoring
at scale
Monitor production models to ensure consistent AI pipeline performance. Monitor data drift to ensure training and live data remain similar and the model continues to deliver as expected.
Keep your models
healthy
Introduce new data to test the performance of an existing model, and easily swap out existing models in a single click when a newer version produces better performance.
Send models to deployment in a single click
Activate batch or real-time insights with the deployment manager that packages and sends everything needed for the AI model to transition from the design and development environment to the production environment.
Model monitoring
at scale
Monitor production models to ensure consistent AI pipeline performance. Monitor data drift to ensure training and live data remain similar and the model continues to deliver as expected.
Keep your models healthy
Introduce new data to test the performance of an existing model, and easily swap out existing models in a single click when a newer version produces better performance.
